From the Department of Biomedical Engineering, Seoul National University College of Medicine, Republic of Korea (W.H.L., M.H.L., S.K.).
Department of Rehabilitation Medicine, Seoul National University Hospital, Republic of Korea (H.G.S., M.Y.S., B.-M.O.).
Stroke. 2020 Feb;51(2):440-448. doi: 10.1161/STROKEAHA.119.027439. Epub 2019 Dec 30.
Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning algorithm. Methods- Consecutive patients (N=137) with acute ischemic stroke referred for swallowing examinations were retrospectively reviewed. Dysphagia was monitored in the 6 months poststroke period and then analyzed using the Kaplan-Meier method and Cox regression model for clinical and radiological factors. Bayesian network models were developed using potential prognostic factors to classify patients into those with good (no need for tube feeding or diet modification for 6 months) and poor (tube feeding or diet modification for 6 months) recovery of swallowing function. Results- Twenty-four (17.5%) patients showed persistent dysphagia for the first 6 months with a mean duration of 65.6 days. The time duration of poststroke dysphagia significantly differed by tube feeding status, clinical dysphagia scale, sex, severe white matter hyperintensities, and bilateral lesions at the corona radiata, basal ganglia, or internal capsule (CR/BG/IC). Among these factors, tube feeding status (<0.001), bilateral lesions at CR/BG/IC (=0.001), and clinical dysphagia scale (=0.042) were significant prognostic factors in a multivariate analysis using Cox regression models. The tree-augmented network classifier, based on 10 factors (sex, lesions at CR, BG/IC, and insula, laterality, anterolateral territory of the brain stem, bilateral lesions at CR/BG/IC, severe white matter hyperintensities, clinical dysphagia scale, and tube feeding status), performed better than other benchmarking classifiers developed in this study. Conclusions- Initial dysphagia severity and bilateral lesions at CR/BG/IC are revealed to be significant prognostic factors for 6-month swallowing recovery. The prediction of 6-month swallowing recovery was feasible based on clinical and radiological factors using the Bayesian network model. We emphasize the importance of bilateral subcortical lesions as prognostic factors that can be utilized to develop prediction models for long-term swallowing recovery.
本研究旨在探讨影响脑卒中后吞咽障碍患者长期吞咽恢复的临床和影像学预后因素，并利用机器学习算法开发和验证一种预后模型。方法：回顾性分析了 137 例因吞咽障碍而行吞咽检查的急性缺血性脑卒中患者的临床资料。在脑卒中后 6 个月内监测吞咽障碍，并采用 Kaplan-Meier 法和 Cox 回归模型分析临床和影像学因素。利用潜在的预后因素，采用贝叶斯网络模型将患者分为吞咽功能恢复良好（6 个月内无需管饲或饮食调整）和恢复不良（6 个月内需要管饲或饮食调整）两组。结果：24 例（17.5%）患者在脑卒中后前 6 个月内持续存在吞咽障碍，平均持续时间为 65.6 天。管饲状态、临床吞咽障碍量表、性别、严重的脑白质高信号、冠状辐射区（CR）、基底节或内囊（BG/IC）双侧病变等因素与吞咽障碍持续时间显著相关。在 Cox 回归模型多因素分析中，管饲状态（<0.001）、CR/BG/IC 双侧病变（=0.001）和临床吞咽障碍量表（=0.042）是显著的预后因素。基于 10 个因素（性别、CR 病变、BG/IC 和岛叶病变、侧别、脑干前外侧区域、CR/BG/IC 双侧病变、严重脑白质高信号、临床吞咽障碍量表和管饲状态）的树增强网络分类器，比本研究中开发的其他基准分类器性能更好。结论：初始吞咽障碍严重程度和 CR/BG/IC 双侧病变是影响 6 个月吞咽恢复的重要预后因素。基于临床和影像学因素，利用贝叶斯网络模型可以对 6 个月的吞咽恢复进行预测。我们强调了双侧皮质下病变作为预后因素的重要性，这些因素可用于开发长期吞咽恢复的预测模型。
